1. Field of the Invention
This invention relates to phased array sensor systems with signal processing, such as are used with radar, sonar, seismic, medical and communication equipments. More specifically, it addresses means to improve detection of signals and suppression of interference sources in such systems.
2. Description of the Prior Art
Adaptive arrays are widely used in reducing the vulnerability of reception of desired signals to the presence of interfering signals. In prior art phased array radars for moving target indication, it is required to estimate target velocities while at the same time removing signals from unwanted directions. Such unwanted signals typically constitute jammers or localized interference sources and clutter. The combination of spatio-temporal processing which achieves these objectives is referred to as angle-Doppler processing. This is achieved by either cascade processing of signals in the space and time domains sequentially or by joint domain processing. With respect to sequential processing in the spatial domain, the signals from each antenna array element are weighted to achieve the desired effective antenna pattern (beamforming). This establishes the antenna beamwidth and look direction(s). This process also provides for the placement of nulls in the antenna pattern at angles associated with unwanted signals. The temporal domain processing typically involves parallel processing of the signal through a Doppler filter bank for estimation of target velocity. Conventional joint domain methods determine a weight vector of large dimensionality which is applied to the total incoming signal. It is known that although cascade processing is computationally less burdensome than joint domain processing, it is suboptimal with respect to accuracy of performance.
Conventional Sensor Array Processors
In contrast to prior art methods of joint domain processing the presently disclosed joint-domain processing architecture offers much reduced computational burden while at the same time offering new insight into the nature of spatio-temporal filtering in the multichannel case.
In array systems employing recursive adaptation methods, a fundamental tradeoff exists between the rate of change of non-stationary noise/interference fields and the steady state performance of the adapted system. Furthermore in radar and in communications applications there can be an interaction between spatial filter (array weights) adaptation performed at high rates and the signal modulation. A favored approach to avoid array adaptation transients in conventional sensor array systems is to invert an estimate of the covariance of the multichannel data vector (direct matrix inversion--DMI). This requires increased computational burden in comparison with recursive methods. The presently disclosed invention makes use of an inherently non-recursive method which is computationally more efficient than the DMI approach.
Space/Time Processing
Consider a coherent radar system with J spatial channels (each channel is the output of either an individual array element or a sub-array composed of multiple array elements), as indicated in FIG. 1. In a surveillance scenario (see, for example, A. G. Jaffer, M. H. Baker, W. P. Ballance, and J. R. Staub, Adaptive Space-Time Processing Techniques for Airborne Radars, RL Technical Report No. RL-TR-91-162, Rome Laboratory, Griffiss AFB, N.Y., 1991., or M. Rangaswamy, P. Chakravarthi, D. Weiner, L. Cai, H. Wang, and A. Ozturk, Signal Detection in Correlated Gaussian and Non-Gaussian Radar Clutter, RL Technical Report No. RL-TR-93-79, Rome Laboratory, Griffiss AFB, N.Y., 1993), the J-element, discrete-time, baseband, complex-valued, finite-duration, vector sequence {x(n).vertline.n=0, 1, . . . , N-1} is the return from the radar resolution (range-azimuth) cell received at each of the J channels for the duration of the coherent processing interval (CPI), which consists of N data points. In the context of a hypothesis testing formulation, the null hypothesis, denoted as H.sub.0, corresponds to the case of target absent; the alternative hypothesis, denoted as H.sub.1, corresponds to the case of target present. Under the null hypothesis, the vector sequence {x(n)} contains clutter, interference, and noise. Under the alternative hypothesis, {x(n)} also contains target information. The vector sequence is assumed to be zero-mean and Gaussian-distributed under both hypotheses.
In the space-time processing application the objective is to detect the target while canceling the spatial interference and clutter. Conventional means to accomplish this objective determine a set of JN complex-valued weights that are applied to the radar return sequence {x(n)}. These weights implement a beam pattern with nulls placed as close as possible (subject to physical beam pattern constraints) to the direction of arrival of the incoming clutter and interference. These weights also place nulls in the temporal frequency response corresponding to the center Doppler frequency of the clutter and interference.
In the aforementioned reference by Rangaswamy et al., the conventional space-time processing configurations for the detection of a moving target are classified into the following three major categories:
(a) optimum joint-domain configuration, PA1 (b) space-time configuration, and PA1 (c) time-space configuration.
The relevant data vector and covariance matrix definitions for these configurations are presented in FIG. 2. In the optimum joint-domain configuration a spatio-temporal performance criterion (minimum mean-square) is formulated and optimized jointly (for the space and time domains). This results in a JN-dimensional weight vector which is used to generate a dot product with the JN-dimensional vector x formed by concatenating the N random vectors {x(n).vertline.n=0, 1, . . . , N-1}, as defined in FIG. 2. A block diagram for this configuration is presented in FIG. 3 for the case of a known signal, as discussed in the aforementioned reference by Rangaswamy et al.
The other two configurations are approximations to the optimal configuration, based on formulating the problem as a cascade of two separate problems. In the space-time configuration a spatial-domain (beamforming) problem is addressed first, and then a temporal-domain problem (Doppler filter bank) is addressed. An optimum solution is obtained for each of the two separate problems, and the solutions are applied sequentially to the data, as indicated in FIG. 4 (also for the case of a known signal). In the time-space configuration the Doppler filter bank precedes the beamformer. A block diagram for the time-space configuration is presented in FIG. 5 for the known signal case.
Each of the configurations listed above admits approximations defined to reduce the computational burden. This is true even for the space-time and time-space configurations, which are themselves approximations to the optimum joint-domain approach. Two important approximations to the optimum approach are the "block sliding" algorithm proposed in the aforementioned reference by Jaffer et al., and the joint-domain localized generalized likelihood ratio (JDL-GLR) proposed in the aforementioned reference by Rangaswamy et al.
Sensor systems employing what are known as sidelobe cancellers typically fall into the space-time processing architecture category. Such systems consist of a main antenna with high gain and an array of auxiliary antennas with associated channels. The auxiliary antennas are designed so their gain patterns approximate the average sidelobe level of the main antenna gain pattern. The auxiliary antennas can then provide replicas of interfering signals which appear in the sidelobes of the main antenna. These replica signals can then be used to cancel interference in the main antenna. The use of appropriately calculated weights for the auxiliary channels constitutes a spatial filtering process. All Doppler processing is conducted subsequent to the spatial cancellation achieved through use of the auxiliary array.
Recent U.S. Pat. No. 5,216,640 to Donald et al. is an example of a time-space architecture for application to sonar. In the invention, multichannel data is provided by an array of hydrophones. The vector time sequences of each data channel are Fourier transformed and a cross spectral density matrix is then calculated using the resulting spectrum vectors for each channel. Subsequently, inverse beamforming methods are used to process the cross spectral density matrix to form a continuous angle-Doppler map. The inverse beamforming methods taught involve computationally intensive integral calculations and the associated system is designed to track multiple targets without suppression of interfering sources.